Unlock the power of Mixed-Integer Linear Programming (MILP) to solve real-world optimization problems.
The MILP Optimization Handbook is a complete guide for graduate students, researchers, engineers, and data scientists who want to master MILP from theory to practice. Written in a clear and accessible style, this handbook blends rigorous mathematics with hands-on examples, making it both a reference guide and a practical tutorial.
Inside you will discover:
Foundations of MILP: Core concepts, mathematical formulations, and polyhedral theory explained step by step.
Algorithms and Solvers: Deep insights into branch-and-bound, cutting planes, decomposition, and modern solver technologies (Gurobi, CPLEX, OR-Tools, CBC).
Advanced Techniques: Preprocessing, relaxations, decomposition, and robust optimization strategies.
Practical Applications: Real-world case studies in supply chain, energy systems, finance, scheduling, telecommunications, and machine learning.
Hands-On Modeling: Complete Python examples with Pyomo, PuLP, Gurobi, and CPLEX, plus solver tuning tips for better performance.
Beyond MILP: Explore MINLP, hybrid approaches with constraint programming, and future trends in quantum optimization.
This handbook is designed to be the only MILP resource you’ll ever need — whether you are building optimization models in academia, applying advanced decision science in industry, or conducting research at the frontier of operations research.
Why choose this book?
Comprehensive coverage: From basics to state-of-the-art techniques.
Balanced learning: Theory + real code examples + applied case studies.
Practical focus: Clear explanations and reproducible examples for immediate application.
If you want to solve complex optimization problems with confidence and stay ahead in the data-driven world, The MILP Optimization Handbook is your essential companion.
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